Running a successful business means understanding and addressing well the needs of customer. This is true in most business, including those involving off-street car parking business applications. There is a need to understand the behavior of parking users to drive efficiencies into existing and future parking management applications and systems.
Existing parking business applications do include some user profiling capacities, but they are often limited to predefined and well known user behaviors frequently observed in reality. For example, parking operators often assume a recurrent profile of users who arrive Friday afternoon and leave Sunday night. Those would correspond to people parking their car for a weekend, so parking operators can target these users by offering them special discount packages or targeted advertising.
The above case involves the situation of user profiles known a priori. Instead, it is expected that other hidden profiles could be discovered automatically from the data in some manner, such as the use of a clustering algorithm. Furthermore, when those new profiles are discovered by a clustering algorithm, there will still be the problem of their easy interpretation. The discovery process should be transparent to the human decision maker; and it should help decision makers understand why certain users have been grouped together and entail an appropriate offer or proposal.
User profiling using a clustering algorithm in a transportation-related analysis system, where profiles can be created, edited, and explored, plus where automatic tags and automatic textual descriptions can help the operator better understand his data is not available nor has been implemented in current parking management systems. Most systems rely on a manual or static definition of a user profile, and do not offer much flexibility around those definitions. Parking (or public transportation user) profiles are usually developed using data surveys, which can be costly and difficult to be performed in an adequate and timely fashion.